output layer
英 [ˈaʊtpʊt ˈleɪə(r)]
美 [ˈaʊtpʊt ler]
输出层
双语例句
- With the pull methodology, individual nodes have no way of knowing if they are in the input, hidden, or output layer.
使用pull方法则无法知道节点究竟位于输入层、隐藏层还是输出层。 - Output layer to flush itself automatically after every output block.
将使输出层，在每段信息块输出后，自动刷新。 - And when preprocessing intensity images to be recognized were loaded onto the input layer of BP network, recognition results were obtained at output layer.
最后编写了一个实现整个系统的软件，将图像输入软件，根据神经网络检测，输出端即可得到识别结果。 - BP neural network topology, including the input layer ( input), hidden layer ( hide layer) and output layer ( output layer).
BP神经网络模型拓扑结构包括输入层（input）、隐层（hidelayer）和输出层（outputlayer）。 - A learning algorithm was introduced, in which most connection weights of the network are fixed, only those between the output layer and the last hidden layer are needed to be adjusted.
给出了网络的学习算法，网络的大部分权值都是固定的，只有输出层与最后隐层之间的权值需要调节。 - The experiment includes: signal preprocessing algorithms; ANN design, number determination of the input layer, output layer and the hidden layer of the BP network; train of the neural network and analysis of the results.
其中涉及信号预处理算法、人工神经网络设计、进行多气体定性和定量分析的BP网络的输入输出和隐层单元确定方法、网络的训练及实验结果分析等。 - The training of the output layer is the supervised algorithm based on LMS.
输出层的训练是采用基于LMS的监督式数学模型。 - The output layer realizes the integration of the rules;
输出层实现规律的综合； - Weights in the output layer are gained by using the pseudoinverse algorithm.
输出层的权值使用伪逆算法确定。 - The network model consists of three layers: the input layer, the hidden layer, and the output layer.
网络模型由三层构成：输入层、隐含层、输出层。 - Output layer accomplishes output signal convergence of space and time, and it accomplishes system output.
输出层完成隐层输出信号的时、空聚合运算和系统输出。 - In order to improve the classification capability of feed forward neural network, this paper extends the use of multilevel neurons for output layer and hidden layer of multiplayer perceptron network. Training algorithm for quantum neural network is proposed.
为了提高前向神经网络的分类能力，该文将多级神经元扩展使用到多层感知器的输出层和隐含层中，并提出了量子神经网络的学习算法。 - According to the features of neural network, the author design a BP network including input layer, hidden layer and output layer, research how to design and choice the parameters of BP network, and develop the network simulation software.
根据神经网络的应用特点，设计了一个包括输入层、隐层、输出层的BP网络，探讨了BP网络的参数设计与选择。 - To prove this idea, the 3-3-1 network and the connected weights fixed by BP arithmetic are adopted, and the effects of the hidden layer and the output layer on the learning course of neural network will be contrasted.
以3-3-1结构的网络及其用BP算法修正连接权值的过程，对比分析了输出层和隐含层神经元进入饱和后对网络学习过程的影响。 - Based on input layer, latent layer and output layer correlation rule, the structure learning method of stochastic fuzzy neural network considered the effects on the latent layer function, which was very important for engineering applications.
基于输入层、隐层、输出层相互关系准则函数的随机模糊神经网络结构学习算法，综合考虑了输入、输出信号对隐层函数的影响。 - In this method, a multilayer feedforward neural network model with strong mapping, associating, generalizing and summarizing abilities which includes input layer, middle processing layers, output layer and training layer, is established.
在该方法中建立了由输入层、中间处理层、输出层和训练层构成的，且具有很强映射、联想、推广和概括能力的多层式前向神经网络模型。 - First, the basic concept of nerve net has been introduced. And then the detail structure of the BP nerve net and the detail design precept of input and output layer have been given.
首先介绍了神经网络的基本概念，接着给出了神经网络的具体结构和输入输出单元的设计方案。 - A radial basis process neural networks model was proposed, which is a kind of three-layer forward structure constituted of input layer, radial basis function hidden layer and output layer.
提出了一种径向基过程神经元网络，该网络模型为3层前向结构，由输入层、径向基过程神经元隐层和输出层组成。 - This algorithm reduces number of hidden nodes and enhances the BP network convergence speed by re-selecting activation function and adjusting weight value of neurons transformation function, scaling coefficient and displacement parameter in output layer and hidden layer.
通过重新选取神经元的激活函数，对输出层和隐层中神经元转换函数的权值、缩放系数和位移参数进行调整，减少隐层节点数，加快BP网络的收敛速度。 - Then determine the structure of BP neural network layer ( the input layer, hidden layer and output layer, each layer) the number of nodes, thereby completing the structure of BP neural network model design.
随后确定了BP神经网络结构的层数（输入层、输出层、隐层）和各层的节点数，从而完成了BP神经网络结构模型的设计。 - Sampling time point of each factor ( such as: the total system load, the system actual output, etc.) and the product of its weight as input layer neurons, and the corresponding time point of the price for the output layer neurons.
采样时刻点的各影响因素（如：系统总负荷、系统实际出力等）与其权重乘积之和作为输入层的神经元，其相应时刻点的电价为输出层神经元。 - Finally the network topology structure was determined as follows: the three nerve network model of one input layer, one hidden layer, one output layer.
最终确定网络的拓扑结构为：一个输入层、一个隐含层、一个输出层的三层神经网络。 - A three-layer architecture which consists of an input layer, a hidden layer and an output layer, is adopted in our neural network.
本文采用包含输入层，隐藏层和输出层的三层式神经网络结构。 - Determined the number of network input layer, output layer neuron, the training data samples were obtained from the hidden layer neuron number.
在设计调节框图时确定网络输入层、输出层神经元个数，由数据样本的训练得到隐含层神经元个数。 - This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier structure of the neural network, namely input layer, hidden layer and output layer.
本文指出了目前传统的数理统计在股票分析上的不足，使用BP神经网络算法对股市进行预测，通过建立一个三层结构的神经网络，即输入层、隐含层和输出层。 - For the fuzzy neural network expert system, the number of neurons for input layer, output layer and hidden layer are determined based on the amount of fault types and the input variables needed for fault diagnosis.
根据所归纳整理的故障类型和故障诊断所需的输入量，确定整个模糊神经网络系统模型的输入层神经元个数、输出层神经元个数、网络的层数以及隐层的神经元个数。 - The network is capable of identifying time-varying dynamics, its learning speed is improved by the linear connection of hidden layer and output layer.
该网络对于时变动态系统具有良好的辨识能力，并且通过对输入层和输出层进行线性连接提高了网络的学习速度。 - Then the design principle, method and implementation process when neural network is using in the prediction of soil water content is elaborated, focusing on input/ output layer, method for determining the number of hidden layer nodes of the neural network. 4.
详细阐述了神经网络应用于土壤含水量预测过程中的设计原则、设计方法和实现过程。重点论述了用于土壤含水量预报的神经网络的输入/输出层、隐含层节点数的确定方法。 - In this article, the traditional nerve network is improved. A network is designed for each of the human faces to classify. Activation functions and nodes of nerve cells of the input layer, hidden layer and output layer are designed respectively.
本文在传统的神经网络上进行改进，对每个人的人脸设计一个网络进行分类，并分别对输入层、隐藏层以及输出层的激励函数和神经元节点数分别进行设计。 - This model includes the input layer, hidden layer and output layer.
该模型包括输入层、隐含层和输出层。